Unlocking the diagnostic power of plasma extracellular vesicle miR-200 family in pancreatic ductal adenocarcinoma.

BACKGROUND: Distinguishing benign from malignant pancreaticobiliary disease is challenging because of the absence of reliable biomarkers. Circulating extracellular vesicles (EVs) have emerged as functional mediators between cells. Their cargos, including microRNAs (miRNAs), are increasingly acknowledged as an important source of potential biomarkers. This multicentric, prospective study aimed to establish a diagnostic plasma EV-derived miRNA signature to discriminate pancreatic ductal adenocarcinoma (PDAC) from benign pancreaticobiliary disease. METHODS: Plasma EVs were isolated using size exclusion chromatography (SEC) and characterised using nanoparticle tracking analysis, electron microscopy and Western blotting. EV-RNAs underwent small RNA sequencing to discover differentially expressed markers for PDAC (n = 10 benign vs. 10 PDAC). Candidate EV-miRNAs were then validated in a cohort of 61 patients (n = 31 benign vs. 30 PDAC) by RT-qPCR. Logistic regression and optimal thresholds (Youden Index) were used to develop an EV-miR-200 family model to detect cancer. This model was tested in an independent cohort of 95 patients (n = 30 benign, 33 PDAC, and 32 cholangiocarcinoma). RESULTS: Small RNA sequencing and RT-qPCR showed that EV-miR-200 family members were significantly overexpressed in PDAC vs. benign disease. Combined expression of the EV-miR-200 family showed an AUC of 0.823. In an independent validation cohort, application of this model showed a sensitivity, specificity and AUC of 100%, 88%, and 0.97, respectively, for diagnosing PDAC. CONCLUSIONS: This is the first study to validate plasma EV-miR-200 members as a clinically-useful diagnostic biomarker for PDAC. Further validation in larger cohorts and clinical trials is essential. These findings also suggest the potential utility in monitoring response and/or recurrence.

MicroRNAs as Bile-based biomarkers in pancreaticobiliary cancers (MIRABILE): a cohort study.

BACKGROUND: Biliary obstruction can be due to both malignant and benign pancreaticobiliary disease. Currently, there are no biomarkers that can accurately help make this distinction. MicroRNAs (miRNAs) are stable molecules in tissue and biofluids that are commonly deregulated in cancer. The MIRABILE study aimed to identify miRNAs in bile that can differentiate malignant from benign pancreaticobiliary disease. MATERIALS AND METHODS: There were 111 patients recruited prospectively at endoscopic retrograde cholangiopancreatography (ERCP) or percutaneous transhepatic cholangiography (PTC) for obstructive jaundice, and bile was aspirated for cell-free RNA (cfRNA) extraction and analysis. In a discovery cohort of 78 patients (27 with pancreatic ductal adenocarcinoma (PDAC), 14 cholangiocarcinoma (CCA), 37 benign disease), cfRNA was subjected to small-RNA sequencing. LASSO regression was used to define bile miRNA signatures, and NormFinder to identify endogenous controls. In a second cohort of 87 patients (34 PDAC, 14 CCA, 39 benign disease), RT-qPCR was used for validation. RESULTS: LASSO regression identified 14 differentially-expressed bile miRNAs of which 6 were selected for validation. When comparing malignant and benign pancreaticobiliary disease, bile miR-340 and miR-182 were validated and significantly differentially expressed (P<0.05 and P<0.001, respectively). This generated an AUC of 0.79 (95%CI 0.70-0.88, sensitivity 65%; specificity 82%) in predicting malignant disease. CONCLUSION: Bile collected during biliary drainage contains miRNAs able to differentiate benign from malignant pancreaticobiliary diseases in patients with obstructive jaundice. These bile miRNAs have the potential to increase diagnostic accuracy.

Closed-loop modulation of remote hippocampal representations with neurofeedback.

Humans can remember specific events without acting on them and can influence which memories are retrieved based on internal goals. However, current animal models of memory typically present sensory cues to trigger retrieval and assess retrieval based on action 1-5 . As a result, it is difficult to determine whether measured patterns of neural activity relate to the cue(s), the retrieved memory, or the behavior. We therefore asked whether we could develop a paradigm to isolate retrieval-related neural activity in animals without retrieval cues or the requirement of a behavioral report. To do this, we focused on hippocampal "place cells." These cells primarily emit spiking patterns that represent the animal's current location (local representations), but they can also generate representations of previously visited locations distant from the animal's current location (remote representations) 6-13 . It is not known whether animals can deliberately engage specific remote representations, and if so, whether this engagement would occur during specific brain states. So, we used a closed-loop neurofeedback system to reward expression of remote representations that corresponded to uncued, experimenter-selected locations, and found that rats could increase the prevalence of these specific remote representations over time; thus, demonstrating memory retrieval modulated by internal goals in an animal model. These representations occurred predominately during periods of immobility but outside of hippocampal sharp-wave ripple (SWR) 13-15 events. This paradigm enables future direct studies of memory retrieval mechanisms in the healthy brain and in models of neurological disorders.

An Artificial Intelligence-Supported Medicinal Chemistry Project: An Example for Incorporating Artificial Intelligence Within the Pharmacy Curriculum.

OBJECTIVE: This study aims to integrate and use AI to teach core concepts in a medicinal chemistry course and to increase the familiarity of pharmacy students with AI in pharmacy practice and drug development. Artificial intelligence (AI) is a multidisciplinary science that aims to build software tools that mimic human intelligence. AI is revolutionizing pharmaceutical research and patient care. Hence, it is important to include AI in pharmacy education to prepare a competent workforce of pharmacists with skills in this area. METHODS: AI principles were introduced in a required medicinal chemistry course for first-year pharmacy students. An AI software, KNIME, was used to examine structure-activity relationships for 5 drugs. Students completed a data sheet that required comprehension of molecular structures and drug-protein interactions. These data were then used to make predictions for molecules with novel substituents using AI. The familiarity of students with AI was surveyed before and after this activity. RESULTS: There was an increase in the number of students indicating familiarity with use of AI in pharmacy (before vs after: 25.3% vs 74.5%). The introduction of AI stimulated interest in the course content (> 60% of students indicated increased interest in medicinal chemistry) without compromising the learning outcomes. Almost 70% of students agreed that more AI should be taught in the PharmD curriculum. CONCLUSION: This is a successful and transferable example of integrating AI in pharmacy education without changing the main learning objectives of a course. This approach is likely to stimulate student interest in AI applications in pharmacy.

Lineage-tracing hematopoietic stem cell origins in vivo to efficiently make human HLF+ HOXA+ hematopoietic progenitors from pluripotent stem cells.

The developmental origin of blood-forming hematopoietic stem cells (HSCs) is a longstanding question. Here, our non-invasive genetic lineage tracing in mouse embryos pinpoints that artery endothelial cells generate HSCs. Arteries are transiently competent to generate HSCs for 2.5 days (∼E8.5-E11) but subsequently cease, delimiting a narrow time frame for HSC formation in vivo. Guided by the arterial origins of blood, we efficiently and rapidly differentiate human pluripotent stem cells (hPSCs) into posterior primitive streak, lateral mesoderm, artery endothelium, hemogenic endothelium, and >90% pure hematopoietic progenitors within 10 days. hPSC-derived hematopoietic progenitors generate T, B, NK, erythroid, and myeloid cells in vitro and, critically, express hallmark HSC transcription factors HLF and HOXA5-HOXA10, which were previously challenging to upregulate. We differentiated hPSCs into highly enriched HLF+ HOXA+ hematopoietic progenitors with near-stoichiometric efficiency by blocking formation of unwanted lineages at each differentiation step. hPSC-derived HLF+ HOXA+ hematopoietic progenitors could avail both basic research and cellular therapies.

Interstitial macrophages are a focus of viral takeover and inflammation in COVID-19 initiation in human lung.

Early stages of deadly respiratory diseases including COVID-19 are challenging to elucidate in humans. Here, we define cellular tropism and transcriptomic effects of SARS-CoV-2 virus by productively infecting healthy human lung tissue and using scRNA-seq to reconstruct the transcriptional program in "infection pseudotime" for individual lung cell types. SARS-CoV-2 predominantly infected activated interstitial macrophages (IMs), which can accumulate thousands of viral RNA molecules, taking over 60% of the cell transcriptome and forming dense viral RNA bodies while inducing host profibrotic (TGFB1, SPP1) and inflammatory (early interferon response, CCL2/7/8/13, CXCL10, and IL6/10) programs and destroying host cell architecture. Infected alveolar macrophages (AMs) showed none of these extreme responses. Spike-dependent viral entry into AMs used ACE2 and Sialoadhesin/CD169, whereas IM entry used DC-SIGN/CD209. These results identify activated IMs as a prominent site of viral takeover, the focus of inflammation and fibrosis, and suggest targeting CD209 to prevent early pathology in COVID-19 pneumonia. This approach can be generalized to any human lung infection and to evaluate therapeutics.

Fully automated CT imaging biomarkers for opportunistic prediction of future hip fractures.

OBJECTIVE: Assess automated CT imaging biomarkers in patients who went on to hip fracture, compared with controls. METHODS: In this retrospective case-control study, 6926 total patients underwent initial abdominal CT over a 20-year interval at one institution. A total of 1308 patients (mean age at initial CT, 70.5 ± 12.0 years; 64.4% female) went on to hip fracture (mean time to fracture, 5.2 years); 5618 were controls (mean age 70.3 ± 12.0 years; 61.2% female; mean follow-up interval 7.6 years). Validated fully automated quantitative CT algorithms for trabecular bone attenuation (at L1), skeletal muscle attenuation (at L3), and subcutaneous adipose tissue area (SAT) (at L3) were applied to all scans. Hazard ratios (HRs) comparing highest to lowest risk quartiles and receiver operating characteristic (ROC) curve analysis including area under the curve (AUC) were derived. RESULTS: Hip fracture HRs (95% CI) were 3.18 (2.69-3.76) for low trabecular bone HU, 1.50 (1.28-1.75) for low muscle HU, and 2.18 (1.86-2.56) for low SAT. 10-year ROC AUC values for predicting hip fracture were 0.702, 0.603, and 0.603 for these CT-based biomarkers, respectively. Multivariate combinations of these biomarkers further improved predictive value; the 10-year ROC AUC combining bone/muscle/SAT was 0.733, while combining muscle/SAT was 0.686. CONCLUSION: Opportunistic use of automated CT bone, muscle, and fat measures can identify patients at higher risk for future hip fracture, regardless of the indication for CT imaging. ADVANCES IN KNOWLEDGE: CT data can be leveraged opportunistically for further patient evaluation, with early intervention as needed. These novel AI tools analyse CT data to determine a patient's future hip fracture risk.

Defining Electronic Health Record Standards for Child Health: A State-of-the-Art Review.

BACKGROUND: Improving child health using health information technology (IT) requires a unique set of functionalities that are built into the electronic health record (EHR) and are used to support patient care. In this article, we review and discuss the milestones preceding the development of a new child health EHR standard and describe the salient features of this contemporary standard. METHODS AND RESULTS: The Health Level Seven Pediatric Care Health IT Functional Profile (HL7 PCHIT FP) is an informative standard that encompasses the EHR functions required to care for patients less than 21 years of age in any clinical setting, developed to address the pediatric-specific functionality gaps in the EHR. It includes criteria that support communication between providers and all caregivers, inclusion of pediatric-specific vital signs and diagnosis, support for transition to adult care, and support for reporting and documentation of child abuse or neglect including communication with involved authorities. EHR functionalities for documentation and reporting of newborn screening tests with follow-up for abnormal results and functionality for children with special health care needs with support for identifying patients who may benefit from care coordination are also incorporated, in addition to school-based linkages enabling communication between the health care system and their school environment. CONCLUSION: The HL7 PCHIT FP is envisioned to be another vital step towards improving pediatric health by enhancing EHRs to address the unique health IT needs of children and their health providers. While the HL7 PCHIT FP is the most recently published standard on pediatric EHR systems, standards development is an iterative process, and recommendations for continuous refinement and additional functionalities for the next standards release are encouraged.

Comparing fully automated AI body composition measures derived from thin and thick slice CT image data.

PURPOSE: To compare fully automated artificial intelligence body composition measures derived from thin (1.25 mm) and thick (5 mm) slice abdominal CT data. METHODS: In this retrospective study, fully automated CT-based body composition algorithms for quantifying bone attenuation, muscle attenuation, muscle area, liver attenuation, liver volume, spleen volume, visceral-to-subcutaneous fat ratio (VSR) and aortic calcium were applied to both thin (1.25 × 0.625 mm) and thick (5 × 3 mm) abdominal CT series from two patient cohorts: unenhanced scans in asymptomatic adults undergoing colorectal cancer screening, and post-contrast scans in patients with colorectal cancer. Body composition measures derived from thin and thick slice data were compared, including correlation coefficients and Bland-Altman analysis. RESULTS: A total of 9882 CT scans (mean age, 57.0 years; 4527 women, 5355 men) were evaluated, including 8947 non-contrast and 935 contrast-enhanced CT exams. Very strong positive correlation was observed for all soft tissue measures: muscle attenuation (r2 = 0.97), muscle area (r2 = 0.98), liver attenuation (r2 = 0.99), liver volume (r2 = 0.98) and spleen volume (r2 = 0.99), VSR (r2 = 0.98), and aortic calcium (r2 = 0.92); (p < 0.001 for all). Moderate positive correlation was observed for bone attenuation (r2 = 0.35). Bland-Altman analysis showed strong agreement for muscle attenuation, muscle area, liver attenuation, liver volume and spleen volume. Mean percentage differences amongst body composition measures were less than 5% for VSR (4.6%), muscle area (- 0.5%), liver attenuation (0.4%) and liver volume (2.7%) and less than 10% for muscle attenuation (- 5.5%) and spleen volume (5.1%). For aortic calcium, thick slice overestimated for Agatston scores between 0 and 100 and > 400 burden in 3.1% and 0.3% relative to thin slice, respectively, but underestimated scores between 100 and 400. CONCLUSION: Automated body composition measures derived from thin and thick abdominal CT data are strongly correlated and show agreement, particularly for soft tissue applications, making it feasible to use either series for these CT-based body composition algorithms.

Different microbial communities in paddy soils under organic and nonorganic farming.

Organic agriculture is a farming method that provides healthy food and is friendly to the environment, and it is developing rapidly worldwide. This study compared microbial communities in organic farming (Or) paddy fields to those in nonorganic farming (Nr) paddy fields based on 16S rDNA sequencing and analysis. The predominant microorganisms in both soils were Proteobacteria, Chloroflexi, Acidobacteria, Actinobacteria, and Nitrospirota. The alpha diversity of the paddy soil microbial communities was not different between the nonorganic and organic farming systems. The beta diversity of nonmetric multidimensional scaling (NMDS) revealed that the two groups were significantly separated. Distance-based redundancy analysis (db-RDA) suggested that soil pH and electrical conductivity (EC) had a positive relationship with the microbes in organic paddy soils. There were 23 amplicon sequence variants (ASVs) that showed differential abundance. Among them, g_B1-7BS (Proteobacteria), s_Sulfuricaulis limicola (Proteobacteria), g_GAL15 (p_GAL15), c_Thermodesulfovibrionia (Nitrospirota), two of f_Anaerolineaceae (Chloroflexi), and two of g_S085 (Chloroflexi) showed that they were more abundant in organic soils, whereas g_11-24 (Acidobacteriota), g__Subgroup_7 (Acidobacteriota), and g_Bacillus (Firmicutes) showed differential abundance in nonorganic paddy soils. Functional prediction of microbial communities in paddy soils showed that functions related to carbohydrate metabolism could be the major metabolic activities. Our work indicates that organic farming differs from nonorganic farming in terms of microbial composition in paddy soils and provides specific microbes that might be helpful for understanding soil fertility.

Bile Microbiome Signatures Associated with Pancreatic Ductal Adenocarcinoma Compared to Benign Disease: A UK Pilot Study.

Pancreatic ductal adenocarcinoma (PDAC) has a very poor survival. The intra-tumoural microbiome can influence pancreatic tumourigenesis and chemoresistance and, therefore, patient survival. The role played by bile microbiota in PDAC is unknown. We aimed to define bile microbiome signatures that can effectively distinguish malignant from benign tumours in patients presenting with obstructive jaundice caused by benign and malignant pancreaticobiliary disease. Prospective bile samples were obtained from 31 patients who underwent either Endoscopic Retrograde Cholangiopancreatography (ERCP) or Percutaneous Transhepatic Cholangiogram (PTC). Variable regions (V3-V4) of the 16S rRNA genes of microorganisms present in the samples were amplified by Polymerase Chain Reaction (PCR) and sequenced. The cohort consisted of 12 PDAC, 10 choledocholithiasis, seven gallstone pancreatitis and two primary sclerosing cholangitis patients. Using the 16S rRNA method, we identified a total of 135 genera from 29 individuals (12 PDAC and 17 benign). The bile microbial beta diversity significantly differed between patients with PDAC vs. benign disease (Permanova p = 0.0173). The separation of PDAC from benign samples is clearly seen through unsupervised clustering of Aitchison distance. We found three genera to be of significantly lower abundance among PDAC samples vs. benign, adjusting for false discovery rate (FDR). These were Escherichia (FDR = 0.002) and two unclassified genera, one from Proteobacteria (FDR = 0.002) and one from Enterobacteriaceae (FDR = 0.011). In the same samples, the genus Streptococcus (FDR = 0.033) was found to be of increased abundance in the PDAC group. We show that patients with obstructive jaundice caused by PDAC have an altered microbiome composition in the bile compared to those with benign disease. These bile-based microbes could be developed into potential diagnostic and prognostic biomarkers for PDAC and warrant further investigation.

A bile-based microRNA signature for differentiating malignant from benign pancreaticobiliary disease.

Differentiating between pancreatic ductal adenocarcinoma (PDAC) and cholangiocarcinoma (CCA) is crucial for the appropriate course of treatment, especially with advancements in the role of neoadjuvant chemotherapies for PDAC, compared to CCA. Furthermore, benign pancreaticobiliary diseases can mimic malignant disease, and indeterminate lesions may require repeated investigations to achieve a diagnosis. As bile flows in close proximity to these lesions, we aimed to establish a bile-based microRNA (miRNA) signature to discriminate between malignant and benign pancreaticobiliary diseases. We performed miRNA discovery by global profiling of 800 miRNAs using the NanoString nCounter platform in prospectively collected bile samples from malignant (n = 43) and benign (n = 14) pancreaticobiliary disease. Differentially expressed miRNAs were validated by RT-qPCR and further assessed in an independent validation cohort of bile from malignant (n = 37) and benign (n = 38) pancreaticobiliary disease. MiR-148a-3p was identified as a discriminatory marker that effectively distinguished malignant from benign pancreaticobiliary disease in the discovery cohort (AUC = 0.797 [95% CI 0.68-0.92]), the validation cohort (AUC = 0.772 [95% CI 0.66-0.88]), and in the combined cohorts (AUC = 0.752 [95% CI 0.67-0.84]). We also established a two-miRNA signature (miR-125b-5p and miR-194-5p) that distinguished PDAC from CCA (validation: AUC = 0.815 [95% CI 0.67-0.96]; and combined cohorts: AUC = 0.814 [95% CI 0.70-0.93]). Our research stands as the largest, multicentric, global profiling study of miRNAs in the bile from patients with pancreaticobiliary disease. We demonstrated their potential as clinically useful diagnostic tools for the detection and differentiation of malignant pancreaticobiliary disease. These bile miRNA biomarkers could be developed to complement current approaches for diagnosing pancreaticobiliary cancers.

CT image-based biomarkers acquired by AI-based algorithms for the opportunistic prediction of falls.

Objective: Evaluate whether biomarkers measured by automated artificial intelligence (AI)-based algorithms are suggestive of future fall risk. Methods: In this retrospective age- and sex-matched case-control study, 9029 total patients underwent initial abdominal CT for a variety of indications over a 20-year interval at one institution. 3535 case patients (mean age at initial CT, 66.5 ± 9.6 years; 63.4% female) who went on to fall (mean interval to fall, 6.5 years) and 5494 controls (mean age at initial CT, 66.7 ± 9.8 years; 63.4% females; mean follow-up interval, 6.6 years) were included. Falls were identified by electronic health record review. Validated and fully automated quantitative CT algorithms for skeletal muscle, adipose tissue, and trabecular bone attenuation at the level of L1 were applied to all scans. Uni- and multivariate assessment included hazard ratios (HRs) and area under the receiver operating characteristic (AUROC) curve. Results: Fall HRs (with 95% CI) for low muscle Hounsfield unit, high total adipose area, and low bone Hounsfield unit were 1.82 (1.65-2.00), 1.31 (1.19-1.44) and 1.91 (1.74-2.11), respectively, and the 10-year AUROC values for predicting falls were 0.619, 0.556, and 0.639, respectively. Combining all these CT biomarkers further improved the predictive value, including 10-year AUROC of 0.657. Conclusion: Automated abdominal CT-based opportunistic measures of muscle, fat, and bone offer a novel approach to risk stratification for future falls, potentially by identifying patients with osteosarcopenic obesity. Advances in knowledge: There are few well-established clinical tools to predict falls. We use novel AI-based body composition algorithms to leverage incidental CT data to help determine a patient's future fall risk.

Prospective isolation of neural stem and progenitor cells from the developing human brain.

Prospective isolation of defined cell types is critical for the functional study of stem cells, especially in primary human tissues. Here, we present a protocol for purifying 10 transcriptomically and functionally distinct neural stem and progenitor cell types from the developing human brain using fluorescence-activated cell sorting. We describe steps for tissue dissociation, staining, and cell sorting as well as downstream functional experiments for measuring clonogenicity, differentiation, and engraftment potential of purified populations. For complete details on the use and execution of this protocol, please refer to Liu et al. (2023).1.

Estimate of the incidence of PANDAS and PANS in 3 primary care populations.

Objective: Pediatric Autoimmune Neuropsychiatric Disorder Associated with Streptococcal infection (PANDAS) and Pediatric Acute-Onset Neuropsychiatric syndrome (PANS) are presumed autoimmune complications of infection or other instigating events. To determine the incidence of these disorders, we performed a retrospective review for the years 2017-2019 at three academic medical centers. Methods: We identified the population of children receiving well-child care at each institution. Potential cases of PANS and PANDAS were identified by including children age 3-12 years at the time they received one of five new diagnoses: avoidant/restrictive food intake disorder, other specified eating disorder, separation anxiety disorder of childhood, obsessive-compulsive disorder, or other specified disorders involving an immune mechanism, not elsewhere classified. Tic disorders was not used as a diagnostic code to identify cases. Data were abstracted; cases were classified as PANDAS or PANS if standard definitions were met. Results: The combined study population consisted of 95,498 individuals. The majority were non-Hispanic Caucasian (85%), 48% were female and the mean age was 7.1 (SD 3.1) years. Of 357 potential cases, there were 13 actual cases [mean age was 6.0 (SD 1.8) years, 46% female and 100% non-Hispanic Caucasian]. The estimated annual incidence of PANDAS/PANS was 1/11,765 for children between 3 and 12 years with some variation between different geographic areas. Conclusion: Our results indicate that PANDAS/PANS is a rare disorder with substantial heterogeneity across geography and time. A prospective investigation of the same question is warranted.

MRI-based vertebral bone quality score: relationship with age and reproducibility.

Vertebral bone quality (VBQ) score is an opportunistic measure of bone mineral density using routine preoperative MRI in spine surgery. VBQ score positively correlates with age and is reproducible across serial scans. However, extrinsic factors, including MRI machine and protocol, affect the VBQ score and must be standardized. PURPOSE: The purposes of this study were to determine whether VBQ score increased with age and whether VBQ remained consistent across serial MRI studies obtained within 3 months. METHODS: This retrospective study evaluated 136 patients, age 20-69, who received two T1-weighted lumbar MRI within 3 months of each other between January 2011 and December 2021. VBQ(L1-4) score was calculated as the quotient of L1-L4 signal intensity (SI) and L3 cerebral spinal fluid (CSF) SI. VBQ(L1) score was calculated as the quotient of L1 SI and L1 CSF SI. Regression analysis was performed to determine correlation of VBQ(L1-4) score with age. Coefficient of variation (CV) was used to determine reproducibility between VBQ(L1-4) scores from serial MRI scans. RESULTS: One hundred thirty-six patients (mean ± SD age 44.9 ± 12.5 years; 53.7% female) were included in this study. Extrinsic factors affecting the VBQ score included patient age, MRI relaxation time, and specific MRI machine. When controlling for MRI relaxation/echo time, the VBQ(L1-4) score was positively correlated with age and had excellent reproducibility in serial MRI with CV of 0.169. There was excellent agreement (ICC > 0.9) of VBQ scores derived from the two formulas, VBQ(L1) and VBQ(L1-4). CONCLUSION: Extrinsic factors, including MRI technical factors and age, can impact the VBQ(L1-4) score and must be considered when using this tool to estimate bone mineral density (BMD). VBQ(L1-4) score was positively correlated with age. Reproducibility of the VBQ(L1-4) score across serial MRI is excellent especially when controlling for technical factors, supporting use of the VBQ score in estimating BMD. The VBQ(L1) score was a reliable alternative to the VBQ(L1-4) score.

Cancer cell-derived extracellular vesicles activate hepatic stellate cells in colorectal cancer.

Colorectal cancer (CRC) is the 2nd leading cause of cancer-related deaths worldwide, primarily due to the development of metastatic disease. The liver is the most frequently affected site. The metastatic cascade relies on a complex interaction between the immune system, tumor, and distant organs. Communication between the tumor and the metastatic site can be mediated by tumor-derived extracellular vesicles (EVs) and their cargo. The mechanisms underlying this process are starting to be understood through research that has rapidly expanded over the past 15 years. One crucial aspect is the remodeling of the microenvironment at the site of metastasis, which is essential for the formation of a premetastatic niche and the subsequent establishment of metastatic deposits. In the evaluated study, the authors use cellular experiments and a mouse model to investigate how tumour derived extracellular vesicles and their microRNA contents interact with hepatic stellate cells (HSCs). They demonstrate how this may lead to remodelling of the microenvironment and the formation of colorectal liver metastasis using their experimental model. In this mini review, we examine the current evidence surrounding tumour derived EVs and their effect on the tumour microenvironment to highlight potential areas for future research in CRC and other malignancies.

Depicting a cellular space occupied by condensates.

Condensates have emerged as a new way to understand how cells are organized, and have been invoked to play crucial roles in essentially all cellular processes. In this view, the cell is occupied by numerous assemblies, each composed of member proteins and nucleic acids that preferentially interact with each other. However, available visual representations of condensates fail to communicate the growing body of knowledge about how condensates form and function. The resulting focus on only a subset of the potential implications of condensates can skew interpretations of results and hinder the generation of new hypotheses. Here we summarize the discussion from a workshop that brought together cell biologists, visualization and computation specialists, and other experts who specialize in thinking about space and ways to represent it. We place the recent advances in condensate research in a historical perspective that describes evolving views of the cell; highlight different attributes of condensates that are not well-served by current visual conventions; and survey potential approaches to overcome these challenges. An important theme of these discussions is that the new understanding on the roles of condensates exposes broader challenges in visual representations that apply to cell biological research more generally.

Cell-free DNA as a potential biomarker for acute myocardial infarction: A systematic review and meta-analysis.

Background: Tissue necrosis releases cell-free deoxyribonucleic acid (cfDNA), leading to rapid increases in plasma concentration with clearance independent of kidney function. Aim: To explore the diagnostic role of cfDNA in acute myocardial infarction (AMI). Methods: This systematic review and meta-analysis included studies of cfDNA in patients with AMI and a comparator group without AMI. The quality assessment of diagnostic accuracy studies-2 (QUADAS-2) tool was used, with AMI determined from the criteria of the original study. Standardised mean differences (SMD) were obtained using a random-effects inverse variance model. Heterogeneity was reported as I2. Pooled sensitivity and specificity were computed using a bivariate model. The area under the curve (AUC) was estimated from a hierarchical summary receiver operating characteristics curve. Results: Seventeen studies were identified involving 1804 patients (n = 819 in the AMI group, n = 985 in the comparator group). Circulating cfDNA concentrations were greater in the AMI group (SMD 3.47 (95%CI: 2.54-4.41, p < 0.001)). The studies were of variable methodological quality with substantial heterogeneity (I2 = 98%, p < 0.001), possibly due to the differences in cfDNA quantification methodologies (Chi2 25.16, p < 0.001, I2 = 92%). Diagnostic accuracy was determined using six studies (n = 804), which yielded a sensitivity of 87% (95%CI: 72%-95%) and specificity of 96% (95%CI: 92%-98%). The AUC was 0.96 (95%CI: 0.93-0.98). Two studies reported a relationship between peak cfDNA and peak troponin. No studies reported data for patients with pre-existing kidney impairment. Conclusion: Plasma cfDNA appears to be a reliable biomarker of myocardial injury. Inferences from existing results are limited owing to methodology heterogeneity.

Block Aligner: an adaptive SIMD-accelerated aligner for sequences and position-specific scoring matrices.

MOTIVATION: Efficiently aligning sequences is a fundamental problem in bioinformatics. Many recent algorithms for computing alignments through Smith-Waterman-Gotoh dynamic programming (DP) exploit Single Instruction Multiple Data (SIMD) operations on modern CPUs for speed. However, these advances have largely ignored difficulties associated with efficiently handling complex scoring matrices or large gaps (insertions or deletions). RESULTS: We propose a new SIMD-accelerated algorithm called Block Aligner for aligning nucleotide and protein sequences against other sequences or position-specific scoring matrices. We introduce a new paradigm that uses blocks in the DP matrix that greedily shift, grow, and shrink. This approach allows regions of the DP matrix to be adaptively computed. Our algorithm reaches over 5-10 times faster than some previous methods while incurring an error rate of less than 3% on protein and long read datasets, despite large gaps and low sequence identities. AVAILABILITY AND IMPLEMENTATION: Our algorithm is implemented for global, local, and X-drop alignments. It is available as a Rust library (with C bindings) at https://github.com/Daniel-Liu-c0deb0t/block-aligner.